Unverified Commit 4a63939c authored by Bharath Ramsundar's avatar Bharath Ramsundar Committed by GitHub
Browse files

Merge pull request #1006 from lilleswing/pip-tf14 

Pip tf14
parents 5b20c8f9 a861a85c

deepchem/models/tests/test_generalize.py

+20 −1
Original line number Diff line number Diff line
@@ -6,6 +6,8 @@ from __future__ import print_function 
from __future__ import division

from __future__ import unicode_literals



from nose.plugins.attrib import attr



__author__ = "Bharath Ramsundar"

__copyright__ = "Copyright 2016, Stanford University"

__license__ = "MIT"
@@ -189,7 +191,15 @@ class TestGeneralize(unittest.TestCase): 
  #  for score in scores[classification_metric.name]:

  #    assert score > .5



  @attr('slow')

  def test_xgboost_regression(self):

    """

    This test is not actually slow -- but cannot currently run

    on Ubuntu 14.04 with Tensorflow 1.4.0



    See Discussion Here

    https://github.com/deepchem/deepchem/issues/960

    """

    import xgboost

    np.random.seed(123)
@@ -218,8 +228,16 @@ class TestGeneralize(unittest.TestCase): 
    scores = model.evaluate(test_dataset, [regression_metric])

    assert scores[regression_metric.name] < 50



  @attr('slow')

  def test_xgboost_multitask_regression(self):

    """Test that xgboost models can learn on simple multitask regression."""

    """

    Test that xgboost models can learn on simple multitask regression.

    This test is not actually slow -- but cannot currently run

    on Ubuntu 14.04 with Tensorflow 1.4.0



    See Discussion Here

    https://github.com/deepchem/deepchem/issues/960

    """

    import xgboost

    np.random.seed(123)

    n_tasks = 4
@@ -255,6 +273,7 @@ class TestGeneralize(unittest.TestCase): 
    for score in scores[regression_metric.name]:

      assert score < 50



  @attr('slow')

  def test_xgboost_classification(self):

    """Test that sklearn models can learn on simple classification datasets."""

    import xgboost

deepchem/molnet/run_benchmark_models.py

+1 −1
Original line number Diff line number Diff line
@@ -80,7 +80,6 @@ def benchmark_classification(train_dataset, 
  if hyper_parameters is None:

    hyper_parameters = hps[model]

  model_name = model

  import xgboost



  if model_name == 'tf':

    # Loading hyper parameters
@@ -359,6 +358,7 @@ def benchmark_classification(train_dataset, 


    # Building xgboost classification model

    def model_builder(model_dir_xgb):

      import xgboost

      xgboost_model = xgboost.XGBClassifier(

          max_depth=max_depth,

          learning_rate=learning_rate,

devtools/conda-recipe/deepchem/meta.yaml

+6 −0
Original line number Diff line number Diff line
@@ -48,3 +48,9 @@ about: 
  home: https://github.com/deepchem/deepchem

  license: MIT

  summary: 'Deep-learning models for Drug Discovery and Quantum Chemistry '

  description: |

    DeepChem aims to provide a high quality open-source toolchain that

    democratizes the use of deep-learning in drug discovery, materials

    science, quantum chemistry, and biology.

  doc_url: https://deepchem.io/

  dev_url: https:/github.com/deepchem/deepchem

devtools/conda-recipe/mdtraj/meta.yaml

0 → 100644
+71 −0
Original line number Diff line number Diff line 
{% set name = "mdtraj" %}

{% set version = "1.9.1" %}

{% set sha256 = "ca1ae07c5f5ce59940a48388ac9b098f8e22743b5f3ed3f46d5e3d1317b06282" %}



package:

  name: {{ name|lower }}

  version: {{ version }}



source:

  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz

  sha256: {{ sha256 }}

build:

  number: 1

  script: python setup.py install --single-version-externally-managed --record record.txt --disable-openmp

  entry_points:

    - mdconvert = mdtraj.scripts.mdconvert:entry_point

    - mdinspect = mdtraj.scripts.mdinspect:entry_point



requirements:

  build:

    - python {{ environ.get('python_version', '3.5') }}

    - setuptools

    - cython

    - numpy 1.8.*  # [not (win and (py35 or py36))]

    - numpy 1.9.*  # [win and py35]

    - numpy 1.11.*  # [win and py36]

    - zlib 1.2.11

    - msinttypes  # [win and py27]



  run:

    - python {{ environ.get('python_version', '3.5') }}

    - setuptools

    - numpy >=1.8  # [not (win and (py35 or py36))]

    - numpy >=1.9  # [win and py35]

    - numpy >=1.11  # [win and py36]

    - scipy

    - pandas

    - pytables

    - zlib 1.2.11



test:

  imports:

    - mdtraj

  commands:

    - mdconvert -h

    - mdinspect -h

    - conda inspect linkages -p $PREFIX $PKG_NAME  # [not win]

    - conda inspect objects -p $PREFIX $PKG_NAME  # [osx]



about:

  home: https://github.com/mdtraj/mdtraj

  license: LGPL-2.1

  summary: "A modern, open library for the analysis of molecular dynamics trajectories"

  description: |

    This is a build of MDTraj without openmp.  This is needed to run on older linux

    kernels alongside tensorflow 1.4.0 from conda-forge.  Full discussion

    https://github.com/deepchem/deepchem/issues/960



    MDTraj is a python library that allows users to manipulate molecular dynamics

    (MD) trajectories and perform a variety of analyses, including fast RMSD,

    solvent accessible surface area, hydrogen bonding, etc. A highlight of MDTraj

    is the wide variety of molecular dynamics trajectory file formats which are

    supported, including RCSB pdb, GROMACS xtc, tng, and trr, CHARMM / NAMD dcd, AMBER

    binpos, AMBER NetCDF, AMBER mdcrd, TINKER arc and MDTraj HDF5.

  doc_url: http://mdtraj.org/

  dev_url: https://github.com/mdtraj/mdtraj



extra:

  recipe-maintainers:

    - rmcgibbo

    - mpharrigan

scripts/install_deepchem_conda.sh

+4 −5
Original line number Diff line number Diff line
@@ -23,19 +23,18 @@ source activate $envname 
conda install -y -q -c omnia pdbfixer=1.4

conda install -y -q -c conda-forge joblib=0.11

conda install -y -q -c conda-forge six=1.10.0

conda install -y -q -c conda-forge mdtraj=1.8.0

conda install -y -q -c deepchem mdtraj=1.9.1

conda install -y -q -c conda-forge scikit-learn=0.19.1

conda install -y -q -c conda-forge setuptools=36.2.2

conda install -y -q -c conda-forge keras=1.2.2

conda install -y -q -c conda-forge networkx=1.11

conda install -y -q -c conda-forge xgboost=0.6a2

conda install -y -q -c conda-forge pillow=4.3.0

conda install -y -q -c conda-forge pandas=0.19.2

conda install -y -q -c conda-forge $tensorflow=1.3.0

conda install -y -q -c conda-forge pandas=0.22.0

yes | pip install $tensorflow==1.4.0

conda install -y -q -c conda-forge nose=1.3.7

conda install -y -q -c conda-forge nose-timer=0.7.0

conda install -y -q -c conda-forge flaky=3.3.0

conda install -y -q -c conda-forge zlib=1.2.11

conda install -y -q -c conda-forge requests=2.18.4

conda install -y -q -c conda-forge numpy=1.13.3

conda install -y -q -c conda-forge xgboost=0.6a2

conda install -y -q -c rdkit rdkit=2017.09.1

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